Full bandwidth packet handling with server systems including offload processors

ABSTRACT

A distributed system server system for a packet processing without top of rack (TOR) units is disclosed. The system can include a plurality of servers, each having at least one host processor, and a plurality of offload processor modules, each offload processor module having an input-output (IO) port and multiple offload processors, wherein a first offload processor module is connected directly to a second offload processor module through respective IO ports, the first and second offload processors configured to provide bidirectional network packet flow between at least the first and second offload processor modules and at least one host processor without TOR units for an inter-server connection.

PRIORITY CLAIMS

This application claims the benefit of U.S. Provisional PatentApplications 61/753,892 filed on Jan. 17, 2013, 61/753,895 filed on Jan.17, 2013, 61/753,899 filed on Jan. 17, 2013, 61/753,901 filed on Jan.17, 2013, 61/753,903 filed on Jan. 17, 2013, 61/753,904 filed on Jan.17, 2013, 61/753,906 filed on Jan. 17, 2013, 61/753,907 filed on Jan.17, 2013, and 61/753,910 filed on Jan. 17, 2013, the contents all ofwhich are incorporated by reference herein.

TECHNICAL FIELD

Described embodiments relate to rack level or cluster level serversystems with network packet processing provided in part by a memory busconnected module with offload processors.

BACKGROUND

Efficient managing of network packet flow and processing is critical forhigh performance networked computing systems. Network packet flow ishighly variable, depending on hardware configurations, process flows anddata flows, with data processing needs varying over several orders ofmagnitude on time scales that can range from seconds to hours.Substantial improvements in network service would be made possible bysystems that can flexibly process a data flow, recognize or characterizepatterns in the data flow, and improve routing and processing decisionsfor the data flow.

Unfortunately, the tree-like server connection topology often used inconventional data centers can be prone to traffic slowdowns andcomputational bottlenecks. Typically, all the servers in such datacenters communicate with each other through higher level Ethernet-typeswitches, such as Top-Of-Rack (TOR) switches. Flow of all the trafficthrough such TOR switches leads to congestion resulting in increasednetwork latency, particularly during the periods of high usage. Further,these switches are expensive and often need replacement to accommodateupgrades to higher network speeds.

SUMMARY

This disclosure describes embodiments of systems, hardware and methodssuitable to create a rack server system for a packet processing. Therack server system has multiple servers with a top of rack (TOR) unitconnectable to each of the servers. Multiple offload processor modulesare provided, with each offload processor module having an input-output(IO) port and multiple offload processors. One offload processor modulecan be directly connected to another offload processor module throughrespective IO ports. In certain embodiments, offload processors can beconnected to the servers through a memory bus, and can further bemounted in a dual in line memory module (DIMM) or other memory socket.

Typically, one or more offload processor modules are connected to eachserver on the rack. In operation, a midplane switch can be defined forforwarding network packets to one or more of the multiple offloadprocessor modules based on availability of the offload processors. Incertain systems, the offload processor modules can be configured toreceive network packets from the server through a memory bus or from the(IO) port on the offload processor module.

In another embodiment, an inter-rack server system for a packetprocessing can include multiple servers arranged into multiple racks.Multiple TOR units can be connected to each of the multiple servers,with each TOR unit further acting as a TOR switch connecting each of themultiple racks to another of the multiple racks. In this configuration,each offload processor module has an IO port and multiple offloadprocessors, and one offload processor module on a first server on afirst rack can be connected directly to another offload processor moduleon a second server on a second rack, with connection provided throughrespective IO ports.

Another embodiment provides a distributed system server system for apacket processing without top of rack (TOR) units. Multiple servers,each having one or more host processors can support multiple offloadprocessor modules. Each offload processor module has an IO port andmultiple offload processors. One offload processor module can beconnected directly to another offload processor module throughrespective IO ports to provide bidirectional network packet flow to boththe multiple offload processors and the one or more server hostprocessors. This can form an inter-server connection without TOR units.

Another embodiment includes a rack server system for a map/reduce dataprocessing. Multiple servers arranged in a rack can support multipleoffload processor modules. Each offload processor module has an IO portand multiple offload processors, and one first offload processor modulecan be connected directly to another offload processor module throughrespective IO ports to define a midplane switch, with results from mapsteps performed by one offload processor module forwarded through themidplane switch to the other offload processor module to perform reducesteps.

Another embodiment includes a distributed system server system forproviding network overlay services. At least two servers canrespectively support at least one offload processor module capable ofreceiving and processing network packets with a logical identifier. Eachoffload processor module has an IO port and multiple offload processors,and a one offload processor module can be connected directly to anotheroffload processor module through respective IO ports to providebidirectional network packet flow to both of the multiple offloadprocessors modules for processing of packets having logical identifiers.Processed packets from the offload processors are returned to thenetwork through the IO port on the offload processor module.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1-0 shows a rack server system according to an embodiment.

FIG. 1-1 shows multiple racks connected to each other according to anembodiment.

FIG. 1-2 shows a highly interconnected network architecture notrequiring top of rack (TOR) switches, according to an embodiment.

FIGS. 2-0 to 2-3 show server attached offload processor modules capableof being supported by rack servers according to various embodiments.

FIG. 2-4 shows a conventional dual-in-line memory module.

FIG. 2-5 shows a system according to another embodiment.

FIG. 3 shows one particular implementation of a system having a memorybus connected offload processor capable of supporting dataflowscheduling according to an embodiment.

FIG. 4 shows an exemplary flow chart for a map/reduce data processingusing at least one server rack according to an embodiment.

FIGS. 5 and 6 respectively show a system and an exemplary flow chart fornetwork packet processing across multiple servers according toembodiments.

DETAILED DESCRIPTION

Various embodiments of the present invention will now be described indetail with reference to a number of drawings. The embodiments showclusters or racks of servers, with some servers able to supportprocessor modules, systems, and methods having offload modules connectedto a system memory bus. Such modules can include offload processors thatare in addition to any host processors connected to the system memorybus, and that can operate on data transferred over the system memorybus, independent of any host processors. In very particular embodiments,offload processor modules can populate physical sockets or slots forconnecting in-line memory modules (e.g., dual in line memory modules(DIMMs)) to a system memory bus. It will be understood that offloadprocessor modules can reside on individual rack server units or bladesthat in turn reside on racks or individual servers. These can be furthergrouped into clusters and datacenters, which can be spatially located inthe same building, in the same city, or even in different countries. Anygrouping level can be connected to each other, and/or connected topublic or private cloud internets.

FIG. 1-0 is a diagram of a system 100 according to an embodiment. Asystem can include servers (120 a to 120 j) and a top of rack (TOR)switch 122. While servers can take various forms, in a particularembodiment, servers (120 a, 120 b, 120 c, 120 i, and 120 j) can be rackmounted server (i.e., rack server). Each server (120 a to 120 j) canhave multiple input/output (I/O) ports, which can support connectionsbased on Ethernet (or related), Infiniband, Fibre Channel, or otheravailable data and signal transport technologies. Each server (120 a to120 j) can be connected to TOR switch 122, which can interfaces with allthe servers (120 a to 120 j) using connections 130. In particularembodiments, system 100 can represent multiple units mounted in a samerack.

According to embodiments, additional connections are also enabledbetween individual servers by operation of offload processor modules(140 a, 140 b, 140 c, 140 i, 140 j). In the particular embodiment shown,offload processors can provide connections between two servers (e.g.,132 between 120 a and 120 b) or groups of servers (connections 134between 120 c, 120 i, and 120 j) to permit direct data transfer betweenservers using offload processor modules 140 a, 140 b, 140 c, 140 i, 140j. Connections between offload processor modules (140 a, 140 b, 140 c,140 i, 140 j) can be via IO ports for such offload processors.Accordingly, while this embodiment includes a TOR switch 122 for serverto server, or server to multiple server data transfers, alternateembodiments can have no TOR switch 122, as connections 132 or 134 areavailable.

In certain embodiments, in addition to IO ports, the offload processormodules 140 a, 140 b, 140 c, 140 i, 140 j can include a physical in-linemodule connector, a memory interface to the server, multiple offloadprocessors, local memory, and control logic for directing data,including network packets, to memory, server, or offload processors. Inparticular embodiments, a module connector can be compatible with a dualin-line memory module (DIMM) slot of a computing system. Since eachserver typically contains multiple DIMM slots, a mix of offloadprocessor modules and DIMM memory can be supported.

FIG. 1-1 shows a system 150 according to an embodiment. A system 150 caninclude multiple racks 152 connected through their respective TORswitches 154. TOR switches 154 can communicate with each other throughan aggregation layer 160. Aggregation layer 160 may include severalswitches and routers and can act as the interface between an externalnetwork and the server racks 152. In this tree-like topology, data framecommunication between various server units can be routed through thecorresponding TOR switches 154. For example, if a server unit 121 needsto forward a packet to server unit 122 (intra-rack communication), itmay do so via path 162 (dashed line). Communication between server units123 and 124 (inter-rack communication) may happen via path 164 (dottedline).

According to embodiments, to prevent data transfer bottlenecks throughTOR switches, and/or to improvement system performance, directinter-rack and/or intra-rack communication can be enabled by offloadprocessor modules included in the servers. For example, directintra-rack server communication 170 and 172 can be enabled by offloadprocessor modules within servers 125/127 and 124/126, respectively.Inter-rack server communication 174 can be enabled by offload processormodules in servers 124/127. Such offload processor modules can take theform of any of those described herein, or equivalents. As will beappreciated, such data communication via offload processor modules canrequire less time and/or less processing power as compared to TORswitching via aggregation layer transfers. Accordingly, such datatransfers can be executed in a more efficient manner than conventionalsystems.

Advantageously, inter/intra-rack communications via offload servermodules can also reduce the need for additional TOR switches and can beincluded to increase bandwidth and introduce redundancy, particularlysince TOR switches may have to be periodically replaced to handle highernetwork speeds.

FIG. 1-2 shows another system 180 according to an embodiment. A system180 can be a network server architecture that does not include TORswitches for inter-rack or intra-rack communication. FIG. 1-2 shows asystem 180 with a midplane switch architecture. One or more server units182 can support offload processor modules 184 which can act as virtualswitches 186 that are capable of receiving and forwarding networkpackets. Virtual switches 186 can have connections to one another, andin particular embodiments, all the virtual switches 186 can be connectedto one other. Offload processor modules 184 can take the form of any ofthose shown herein, or equivalents. In particular embodiments, offloadprocessor modules 184 are mountable in DIMM sockets of a server unit).Ingress packets can be examined and classified by the offload processormodules 184. Such examination can include deep packet inspection andclassification with a high degree of granularity. This is in contrast toTOR switches in conventional tree-like topologies, which forward packetsbased only on a media access control (MAC) address.

According to some embodiments, a system 180 can include TOR switches,but operation of such switches can be limited to forwarding packets tooffload processor modules 184 such that most or all of any packetprocessing can be handled by the offload processor modules 184. In suchcases, it can be possible to scale up packet handling capabilities byequipping more server units with offload processor modules 184 (orincreasing the number of offload processor modules per server), asneeded. This is in contrast to conventional approaches, which mayupgrade TOR switches, which can be costly.

According to some embodiments, one or more of the offload processormodules can be configured to act as a traffic manager for the midplaneswitch. A traffic manager processor module 188 can monitor the trafficand provide multiple communication paths between servers units 182. Suchan arrangement may have better fault tolerance compared to tree-likenetwork topologies.

In certain embodiments, one or more offload processor modules can beconfigured as layer 3 routers that can route traffic into and out of theserver racks. These offload processor modules 184 can bridge all theinterconnected servers to an external (10 GB or faster) Ethernet-typeconnection.

In some embodiments, inclusion of offload processor modules can enableTOR switches to be omitted from a system 180, with overall network datatransfer and processing speeds being improved over conventionalarchitectures.

As will be understood, many of the processing tasks for execution by anoffload processor or server host processor can be implemented onmultiple threads running on multiple processing cores, with multipleprocessors in each server being supported. Such parallelization of tasksinto multiple thread contexts can provide for increased throughput.Processors architectures such as MIPS may include deep instructionpipelines to improve the number of instructions per cycle. Further, theability to run a multi-threaded programming environment results inenhanced usage of existing processor resources. To further increaseparallel execution on the hardware, processor architecture may includemultiple processor cores. Multi-core architectures including the sametype of cores, referred to as homogeneous core architectures, canprovide higher instruction throughput by parallelizing threads orprocesses across multiple cores. However, in such homogeneous corearchitectures, the shared resources, such as memory, are amortized overa small number of processors. In still other embodiments, multipleoffload or host processors can reside on modules connected to individualrack units or blades that in turn reside on racks or individual servers.These can be further grouped into clusters and datacenters, which can bespatially located in the same building, in the same city, or even indifferent countries. Any grouping level can be connected to each other,and/or connected to public or private cloud internets.

Memory and I/O accesses can incur a high amount of processor overhead.Further, context switches in conventional general purpose processingunits can be computationally intensive. It is therefore desirable toreduce context switch overhead in a networked computing resourcehandling a plurality of networked applications in order to increaseprocessor throughput. Conventional server loads can require complextransport, high memory bandwidth, extreme amounts of data bandwidth(randomly accessed, parallelized, and highly available), but often withlight touch processing: HTML, video, packet-level services, security,and analytics. Further, idle processors still consume more than 50% oftheir peak power consumption.

In contrast, according to embodiments herein, complex transport, databandwidth intensive, frequent random access oriented, “light touch”processing loads can be handled behind a socket abstraction created onmultiple offload processor cores. At the same time, “heavy touch”,computing intensive loads can be handled by a socket abstraction on ahost processor core (e.g., ×86 processor cores). Such software socketscan allow for a natural partitioning of these loads between offloadprocessor (e.g., ARM) cores and host processor (e.g., ×86) cores. Byusage of new application level sockets, according to embodiments, serverloads can be broken up across the offload processing cores and the hostprocessing cores.

FIGS. 2-0 to 2-5 describe aspects of hardware and method embodimentsthat can provide communication interconnections between servers usingmemory bus mounted offload processor modules. Such communications can beinter-rack and/or intra-rack communications. In particular embodiments,offload processor modules can be DIMM mounted modules.

FIGS. 2-0 to 2-5 describe aspects of hardware embodiments of a modulethat can include context switching as described herein. In particularembodiments, such processing modules can include DIMM mountable modules.

FIG. 2-0 is a block diagram of a processing module 200 according to oneembodiment. A processing module 200 can include a physical connector202, a memory interface 204, arbiter logic 206, offload processor(s)208, local memory 210, and control logic 212. A connector 202 canprovide a physical connection to system memory bus. This is in contrastto a host processor which can access a system memory bus via a memorycontroller, or the like. In very particular embodiments, a connector 202can be compatible with a dual in-line memory module (DIMM) slot of acomputing system. Accordingly, a system including multiple DIMM slotscan be populated with one or more processing modules 200, or a mix ofprocessing modules and DIMM modules.

A memory interface 204 can detect data transfers on a system memory bus,and in appropriate cases, enable write data to be stored in theprocessing module 200 and/or read data to be read out from theprocessing module 200. Such data transfers can include the receipt ofpacket data having a particular network identifier. In some embodiments,a memory interface 204 can be a slave interface, thus data transfers arecontrolled by a master device separate from the processing module 200.In very particular embodiments, a memory interface 204 can be a directmemory access (DMA) slave, to accommodate DMA transfers over a systemmemory bus initiated by a DMA master. In some embodiments, a DMA mastercan be a device different from a host processor. In such configurations,processing module 200 can receive data for processing (e.g., DMA write),and transfer processed data out (e.g., DMA read) without consuming hostprocessor resources.

Arbiter logic 206 can arbitrate between conflicting accesses of datawithin processing module 200. In some embodiments, arbiter logic 206 canarbitrate between accesses by offload processor 208 and accessesexternal to the processor module 200. It is understood that a processingmodule 200 can include multiple locations that are operated on at thesame time. It is understood that accesses arbitrated by arbiter logic206 can include accesses to physical system memory space occupied by theprocessor module 200, as well as accesses to other resources (e.g.,cache memory of offload or host processor). Accordingly, arbitrationrules for arbiter logic 206 can vary according to application. In someembodiments, such arbitration rules are fixed for a given processormodule 200. In such cases, different applications can be accommodated byswitching out different processing modules. However, in alternateembodiments, such arbitration rules can be configurable.

Offload processor 208 can include one or more processors that canoperate on data transferred over the system memory bus. In someembodiments, offload processors can run a general operating system orserver applications such as Apache (as but one very particular example),enabling processor contexts to be saved and retrieved. Computing tasksexecuted by offload processor 208 can be handled by the hardwarescheduler. Offload processors 208 can operate on data buffered in theprocessor module 200. In addition or alternatively, offload processors208 can access data stored elsewhere in a system memory space. In someembodiments, offload processors 208 can include a cache memoryconfigured to store context information. An offload processor 208 caninclude multiple cores or one core.

A processor module 200 can be included in a system having a hostprocessor (not shown). In some embodiments, offload processors 208 canbe a different type of processor as compared to the host processor. Inparticular embodiments, offload processors 208 can consume less powerand/or have less computing power than a host processor. In veryparticular embodiments, offload processors 208 can be “wimpy” coreprocessors, while a host processor can be a “brawny” core processor.However, in alternate embodiments, offload processors 208 can haveequivalent computing power to any host processor. In very particularembodiments, a host processor can be an ×86 type processor, while anoffload processor 208 can include an ARM, ARC, Tensilica, MIPS,Strong/ARM, or RISC type processor, as but a few examples.

Local memory 210 can be connected to offload processor 208 to enable thestoring of context information. Accordingly, an offload processor 208can store current context information, and then switch to a newcomputing task, then subsequently retrieve the context information toresume the prior task. In very particular embodiments, local memory 210can be a low latency memory with respect to other memories in a system.In some embodiments, storing of context information can include copyingan offload processor 208 cache.

In some embodiments, a same space within local memory 210 is accessibleby multiple offload processors 208 of the same type. In this way, acontext stored by one offload processor can be resumed by a differentoffload processor.

Control logic 212 can control processing tasks executed by offloadprocessor(s). In some embodiments, control logic 212 can be considered ahardware scheduler that can be conceptualized as including a dataevaluator 214, scheduler 216 and a switch controller 218. A dataevaluator 214 can extract “metadata” from write data transferred over asystem memory bus. “Metadata”, as used herein, can be any informationembedded at one or more predetermined locations of a block of write datathat indicates processing to be performed on all or a portion of theblock of write data and/or indicate a particular task/process to whichthe data belongs (e.g., classification data). In some embodiments,metadata can be data that indicates a higher level organization for theblock of write data. As but one very particular embodiment, metadata canbe header information of one or more network packets (which may or maynot be encapsulated within a higher layer packet structure).

A scheduler 216 (e.g., a hardware scheduler) can order computing tasksfor offload processor(s) 208. In some embodiments, scheduler 216 cangenerate a schedule that is continually updated as write data forprocessing is received. In very particular embodiments, a scheduler 216can generate such a schedule based on the ability to switch contexts ofoffload processor(s) 208. In this way, on-module computing prioritiescan be adjusted on the fly. In very particular embodiments, a scheduler216 can assign a portion of physical address space (e.g., memorylocations within local memory 210) to an offload processor 208,according to computing tasks. The offload processor 208 can then switchbetween such different spaces, saving context information prior to eachswitch, and subsequently restoring context information when returning tothe memory space.

Switch controller 218 can control computing operations of offloadprocessor(s) 208. In particular embodiments, according to scheduler 216,switch controller 218 can order offload processor(s) 208 to switchcontexts. It is understood that a context switch operation can be an“atomic” operation, executed in response to a single command from switchcontroller 218. In addition or alternatively, a switch controller 218can issue an instruction set that stores current context information,recalls context information, etc.

In some embodiments, processor module 200 can include a buffer memory(not shown). A buffer memory can store received write data on board theprocessor module. A buffer memory can be implemented on an entirelydifferent set of memory devices, or can be a memory embedded with logicand/or the offload processor. In the latter case, arbiter logic 206 canarbitrate access to the buffer memory. In some embodiments, a buffermemory can correspond to a portion of a system physical memory space.The remaining portion of the system memory space can correspond to otherlike processor modules and/or memory modules connected to the samesystem memory bus. In some embodiments buffer memory can be differentthan local memory 210. For example, buffer memory can have a sloweraccess time than local memory 210. However, in other embodiments, buffermemory and local memory can be implemented with like memory devices.

In very particular embodiments, write data for processing can have anexpected maximum flow rate. A processor module 200 can be configured tooperate on such data at, or faster than, such a flow rate. In this way,a master device (not shown) can write data to a processor module withoutdanger of overwriting data “in process”.

The various computing elements of a processor module 200 can beimplemented as one or more integrated circuit devices (ICs). It isunderstood that the various components shown in FIG. 2-0 can be formedin the same or different ICs. For example, control logic 212, memoryinterface 214, and/or arbiter logic 206 can be implemented on one ormore logic ICs, while offload processor(s) 208 and local memory 210 areseparate ICs. Logic ICs can be fixed logic (e.g., application specificICs), programmable logic (e.g., field programmable gate arrays, FPGAs),or combinations thereof.

Advantageously, the foregoing hardware and systems can provide improvedcomputational performance as compared to traditional computing systems.Conventional systems, including those based on ×86 processors, are oftenill-equipped to handle such high volume applications. Even idling, ×86processors use a significant amount of power, and near continuousoperation for high bandwidth packet analysis or other high volumeprocessing tasks makes the processor energy costs one of the dominantprice factors.

In addition, conventional systems can have issues with the high cost ofcontext switching wherein a host processor is required to executeinstructions which can include switching from one thread to another.Such a switch can require storing and recalling the context for thethread. If such context data is resident in a host cache memory, such acontext switch can occur relatively quickly. However, if such contextdata is no longer in cache memory (i.e., a cache miss), the data must berecalled from system memory, which can incur a multi-cycle latency.Continuous cache misses during context switching can adversely impactsystem performance.

FIG. 2-1 shows a processor module 200-1 according to one very particularembodiment which is capable of reducing issues associated with highvolume processing or context switching associated with many conventionalserver systems. A processor module 200-1 can include ICs 220-0/1 mountedto a printed circuit board (PCB) type substrate 222. PCB type substrate222 can include in-line module connector 202, which in one veryparticular embodiment, can be a DIMM compatible connector. IC 220-0 canbe a system-on-chip (SoC) type device, integrating multiple functions.In the very particular embodiment shown, an IC 220-0 can includeembedded processor(s), logic and memory. Such embedded processor(s) canbe offload processor(s) 208 as described herein, or equivalents. Suchlogic can be any of controller logic 212, memory interface 204 and/orarbiter logic 206, as described herein, or equivalents. Such memory canbe any of local memory 210, cache memory for offload processor(s) 208,or buffer memory, as described herein, or equivalents. Logic IC 220-1can provide logic functions not included IC 220-0.

FIG. 2-2 shows a processor module 200-2 according to another veryparticular embodiment. A processor module 200-2 can include ICs 220-2,-3, -4, -5 mounted to a PCB type substrate 222, like that of FIG. 2-1.However, unlike FIG. 2-1, processor module functions are distributedamong single purpose type ICs. IC 220-2 can be a processor IC, which canbe an offload processor 208. IC 220-3 can be a memory IC which caninclude local memory 210, buffer memory, or combinations thereof. IC220-4 can be a logic IC which can include control logic 212, and in onevery particular embodiment, can be an FPGA. IC 220-5 can be anotherlogic IC which can include memory interface 204 and arbiter logic 206,and in one very particular embodiment, can also be an FPGA.

It is understood that FIGS. 2-1/2 represent but two of variousimplementations. The various functions of a processor module can bedistributed over any suitable number of ICs, including a single SoC typeIC.

FIG. 2-3 shows an opposing side of a processor module 200-1 or 200-2according to a very particular embodiment. Processor module 200-3 caninclude a number of memory ICs, one shown as 220-6, mounted to a PCBtype substrate 222, like that of FIG. 2-1. It is understood that variousprocessing and logic components can be mounted on an opposing side tothat shown. A memory IC 220-6 can be configured to represent a portionof the physical memory space of a system. Memory ICs 220-6 can performany or all of the following functions: operate independently of otherprocessor module components, providing system memory accessed in aconventional fashion; serve as buffer memory, storing write data thatcan be processed with other processor module components, or serve aslocal memory for storing processor context information.

FIG. 2-4 shows a conventional DIMM module (i.e., it serves only a memoryfunction) that can populate a memory bus along with processor modules asdescribed herein, or equivalents.

FIG. 2-5 shows a system 230 according to one embodiment. A system 230can include a system memory bus 228 accessible via multiple in-linemodule slots (one shown as 226). According to embodiments, any or all ofthe slots 226 can be occupied by a processor module 200 as describedherein, or an equivalent. In the event all slots 226 are not occupied bya processor module 200, available slots can be occupied by conventionalin-line memory modules 224. In a very particular embodiment, slots 226can be DIMM slots.

In some embodiments, a processor module 200 can occupy one slot.However, in other embodiments, a processor module can occupy multipleslots.

In some embodiments, a system memory bus 228 can be further interfacedwith one or more host processors and/or input/output device (not shown).

Having described processor modules according to various embodiments,operations of an offload processor module capable of interfacing withserver or similar system via a memory bus and according to a particularembodiment will now be described.

FIG. 3 shows a system 301 that can execute context switches in offloadprocessors according to an embodiment. In the example shown, a system301 can transport packet data to one or more computational units (oneshown as 300) located on a module, which in particular embodiments, caninclude a connector compatible with an existing memory module. In someembodiments, a computational unit 300 can include a processor module asdescribed in embodiments herein, or an equivalent. A computational unit300 can be capable of intercepting or otherwise accessing packets sentover a memory bus 316 and carrying out processing on such packets,including but not limited to termination or metadata processing. Asystem memory bus 316 can be a system memory bus like those describedherein, or equivalents (e.g., 228).

Referring still to FIG. 3, a system 301 can include an I/O device 302which can receive packet or other I/O data from an external source. Insome embodiments I/O device 302 can include physical or virtualfunctions generated by the physical device to receive a packet or otherI/O data from the network or another computer or virtual machine. In thevery particular embodiment shown, an I/O device 302 can include anetwork interface card (NIC) having input buffer 302 a (e.g., DMA ringbuffer) and an I/O virtualization function 302 b.

According to embodiments, an I/O device 302 can write a descriptorincluding details of the necessary memory operation for the packet (i.e.read/write, source/destination). Such a descriptor can be assigned avirtual memory location (e.g., by an operating system of the system301). I/O device 302 then communicates with an input output memorymanagement unit (IOMMU) 304 which can translate virtual addresses tocorresponding physical addresses with an IOMMU function 304 b. In theparticular embodiment shown, a translation look-aside buffer (TLB) 304 acan be used for such translation. Virtual function reads or writes databetween I/O device and system memory locations can then be executed witha direct memory transfer (e.g., DMA) via a memory controller 306 b ofthe system 301. An I/O device 302 can be connected to IOMMU 304 by ahost bus 312. In one very particular embodiment, a host bus 312 can be aperipheral interconnect (PCI) type bus. IOMMU 304 can be connected to ahost processing section 306 at a central processing unit I/O (CPUIO) 306a. In the embodiment shown, such a connection 314 can support aHyperTransport (HT) protocol.

In the embodiment shown, a host processing section 306 can include theCPUIO 306 a, memory controller 306 b, processing core 306 c andcorresponding provisioning agent 306 d.

In particular embodiments, a computational unit 300 can interface withthe system bus 316 via standard in-line module connection, which in veryparticular embodiments can include a DIMM type slot. In the embodimentshown, a memory bus 316 can be a DDR3 type memory bus. Alternateembodiments can include any suitable system memory bus. Packet data canbe sent by memory controller 306 b via memory bus 316 to a DMA slaveinterface 310 a. DMA slave interface 310 a can be adapted to receiveencapsulated read/write instructions from a DMA write over the memorybus 316.

A hardware scheduler (308 b/c/d/e/h) can perform traffic management onincoming packets by categorizing them according to flow using sessionmetadata. Packets can be queued for output in an onboard memory (310b/308 a/308 m) based on session priority. When the hardware schedulerdetermines that a packet for a particular session is ready to beprocessed by the offload processor 308 i, the onboard memory is signaledfor a context switch to that session. Utilizing this method ofprioritization, context switching overhead can be reduced, as comparedto conventional approaches. That is, a hardware scheduler can handlecontext switching decisions and thus optimize the performance of thedownstream resource (e.g., offload processor 308 i).

As noted above, in very particular embodiments, an offload processor 308i can be a “wimpy core” type processor. According to some embodiments, ahost processor 306 c can be a “brawny core” type processor (e.g., an ×86or any other processor capable of handling “heavy touch” computationaloperations). While an I/O device 302 can be configured to trigger hostprocessor interrupts in response to incoming packets, according toembodiments, such interrupts can be disabled, thereby reducingprocessing overhead for the host processor 306 c. In some veryparticular embodiments, an offload processor 308 i can include an ARM,ARC, Tensilica, MIPS, Strong/ARM or any other processor capable ofhandling “light touch” operations. Preferably, an offload processor canrun a general purpose operating system for executing a plurality ofsessions, which can be optimized to work in conjunction with thehardware scheduler in order to reduce context switching overhead.

Referring still to FIG. 3, in operation, a system 301 can receivepackets from an external network over a network interface. The packetsare destined for either a host processor 306 c or an offload processor308 i based on the classification logic and schematics employed by I/Odevice 302. In particular embodiments, I/O device 302 can operate as avirtualized NIC, with packets for a particular logical network or to acertain virtual MAC (VMAC) address can be directed into separate queuesand sent over to the destination logical entity. Such an arrangement cantransfer packets to different entities. In some embodiments, each suchentity can have a virtual driver, a virtual device model that it uses tocommunicate with connected virtual network.

According to embodiments, multiple devices can be used to redirecttraffic to specific memory addresses. So, each of the network devicesoperates as if it is transferring the packets to the memory location ofa logical entity. However, in reality, such packets are transferred tomemory addresses where they can be handled by one or more offloadprocessors (e.g., 308 i). In particular embodiments such transfers areto physical memory addresses, thus logical entities can be removed fromthe processing, and a host processor can be free from such packethandling.

Accordingly, embodiments can be conceptualized as providing a memory“black box” to which specific network data can be fed. Such a memoryblack box can handle the data (e.g., process it) and respond back whensuch data is requested.

Referring still to FIG. 3, according to some embodiments, I/O device 302can receive data packets from a network or from a computing device. Thedata packets can have certain characteristics, including transportprotocol number, source and destination port numbers, source anddestination IP addresses, for example. The data packets can further havemetadata that is processed (308 d) that helps in their classificationand management.

I/O device 302 can include, but is not limited to, peripheral componentinterconnect (PCI) and/or PCI express (PCIe) devices connecting with ahost motherboard via PCI or PCIe bus (e.g., 312). Examples of I/Odevices include a network interface controller (NIC), a host busadapter, a converged network adapter, an ATM network interface, etc.

In order to provide for an abstraction scheme that allows multiplelogical entities to access the same I/O device 302, the I/O device maybe virtualized to provide for multiple virtual devices each of which canperform some of the functions of the physical I/O device. The IOvirtualization program (e.g., 302 b) according to an embodiment, canredirect traffic to different memory locations (and thus to differentoffload processors attached to modules on a memory bus). To achievethis, an I/O device 302 (e.g., a network card) may be partitioned intoseveral function parts; including controlling function (CF) supportinginput/output virtualization (IOV) architecture (e.g., single-root IOV)and multiple virtual function (VF) interfaces. Each virtual functioninterface may be provided with resources during runtime for dedicatedusage. Examples of the CF and VF may include the physical function andvirtual functions under schemes such as Single Root I/O Virtualizationor Multi-Root I/O Virtualization architecture. The CF acts as thephysical resources that sets up and manages virtual resources. The CF isalso capable of acting as a full-fledged 10 device. The VF isresponsible for providing an abstraction of a virtual device forcommunication with multiple logical entities/multiple memory regions.

The operating system/the hypervisor/any of the virtual machines/usercode running on a host processor 306 c may be loaded with a devicemodel, a VF driver and a driver for a CF. The device model may be usedto create an emulation of a physical device for the host processor 306 cto recognize each of the multiple VFs that are created. The device modelmay be replicated multiple times to give the impression to a VF driver(a driver that interacts with a virtual IO device) that it isinteracting with a physical device of a particular type.

For example, a certain device module may be used to emulate a networkadapter such as the Intel® Ethernet Converged Network Adapter (CNA)X540-T2, so that the I/O device 302 believes it is interacting with suchan adapter. In such a case, each of the virtual functions may have thecapability to support the functions of the above said CNA, i.e., each ofthe Physical Functions should be able to support such functionality. Thedevice model and the VF driver can be run in either privileged ornon-privileged mode. In some embodiments, there is no restriction withregard to who hosts/runs the code corresponding to the device model andthe VF driver. The code, however, has the capability to create multiplecopies of device model and VF driver so as to enable multiple copies ofsaid I/O interface to be created.

An application or provisioning agent 306 d, as part of anapplication/user level code running in a kernel, may create a virtualI/O address space for each VF, during runtime and allocate part of thephysical address space to it. For example, if an application handlingthe VF driver instructs it to read or write packets from or to memoryaddresses 0xaaaa to 0xffff, the device driver may write I/O descriptorsinto a descriptor queue with a head and tail pointer that are changeddynamically as queue entries are filled. The data structure may be ofanother type as well, including but not limited to a ring structure 302a or hash table.

The VF can read from or write data to the address location pointed to bythe driver. Further, on completing the transfer of data to the addressspace allocated to the driver, interrupts, which are usually triggeredto the host processor to handle said network packets, can be disabled.Allocating a specific I/O space to a device can include allocating saidIO space a specific physical memory space occupied.

In another embodiment, the descriptor may comprise only a writeoperation, if the descriptor is associated with a specific datastructure for handling incoming packets. Further, the descriptor foreach of the entries in the incoming data structure may be constant so asto redirect all data write to a specific memory location. In analternate embodiment, the descriptor for consecutive entries may pointto consecutive entries in memory so as to direct incoming packets toconsecutive memory locations.

Alternatively, said operating system may create a defined physicaladdress space for an application supporting the VF drivers and allocatea virtual memory address space to the application or provisioning agent306 d, thereby creating a mapping for each virtual function between saidvirtual address and a physical address space. Said mapping betweenvirtual memory address space and physical memory space may be stored inIOMMU tables (e.g., a TLB 304 a). The application performing memoryreads or writes may supply virtual addresses to say virtual function,and the host processor OS may allocate a specific part of the physicalmemory location to such an application.

Alternatively, VF may be configured to generate requests such as readand write which may be part of a direct memory access (DMA) read orwrite operation, for example. The virtual addresses is be translated bythe IOMMU 304 to their corresponding physical addresses and the physicaladdresses may be provided to the memory controller for access. That is,the IOMMU 304 may modify the memory requests sourced by the I/O devicesto change the virtual address in the request to a physical address, andthe memory request may be forwarded to the memory controller for memoryaccess. The memory request may be forwarded over a bus 314 that supportsa protocol such as HyperTransport 314. The VF may in such cases carryout a direct memory access by supplying the virtual memory address tothe IOMMU 304.

Alternatively, said application may directly code the physical addressinto the VF descriptors if the VF allows for it. If the VF cannotsupport physical addresses of the form used by the host processor 306 c,an aperture with a hardware size supported by the VF device may be codedinto the descriptor so that the VF is informed of the target hardwareaddress of the device. Data that is transferred to an aperture may bemapped by a translation table to a defined physical address space in thesystem memory. The DMA operations may be initiated by software executedby the processors, programming the I/O devices directly or indirectly toperform the DMA operations.

Referring still to FIG. 3, in particular embodiments, parts ofcomputational unit 300 can be implemented with one or more FPGAs. In thesystem of FIG. 3, computational unit 300 can include FPGA 310 in whichcan be formed a DMA slave device module 310 a and arbiter 310 f. A DMAslave module 310 a can be any device suitable for attachment to a memorybus 316 that can respond to DMA read/write requests. In alternateembodiments, a DMA slave module 310 a can be another interface capableof block data transfers over memory bus 316. The DMA slave module 310 acan be capable of receiving data from a DMA controller (when it performsa read from a ‘memory’ or from a peripheral) or transferring data to aDMA controller (when it performs a write instruction on the DMA slavemodule 310 a). The DMA slave module 310 a may be adapted to receive DMAread and write instructions encapsulated over a memory bus, (e.g., inthe form of a DDR data transmission, such as a packet or data burst), orany other format that can be sent over the corresponding memory bus.

A DMA slave module 310 a can reconstruct the DMA read/write instructionfrom the memory R/W packet. The DMA slave module 310 a may be adapted torespond to these instructions in the form of data reads/data writes tothe DMA master, which could either be housed in a peripheral device, inthe case of a PCIe bus, or a system DMA controller in the case of an ISAbus.

I/O data that is received by the DMA device 310 a can then queued forarbitration. Arbitration can include the process of scheduling packetsof different flows, such that they are provided access to availablebandwidth based on a number of parameters. In general, an arbiter 310 fprovides resource access to one or more requestors. If multiplerequestors request access, an arbiter 310 f can determine whichrequestor becomes the accessor and then passes data from the accessor tothe resource interface, and the downstream resource can begin executionon the data. After the data has been completely transferred to aresource, and the resource has competed execution, the arbiter 310 f cantransfer control to a different requestor and this cycle repeats for allavailable requestors. In the embodiment of FIG. 3 arbiter 310 f cannotify other portions of computational unit 300 (e.g., 308) of incomingdata.

Alternatively, a computation unit 300 can utilize an arbitration schemeshown in U.S. Pat. No. 7,813,283, issued to Dalal on Oct. 12, 2010, thecontents of which are incorporated herein by reference. Other suitablearbitration schemes known in art could be implemented in embodimentsherein. Alternatively, the arbitration scheme of the current inventionmight be implemented using an OpenFlow switch and an OpenFlowcontroller.

In the very particular embodiment of FIG. 3, computational unit 300 canfurther include notify/prefetch circuits 310 c which can prefetch datastored in a buffer memory 310 b in response to DMA slave module 310 a,and as arbitrated by arbiter 310 f. Further, arbiter 310 f can accessother portions of the computational unit 300 via a memory mapped I/Oingress path 310 e and egress path 310 g.

Referring to FIG. 3, a hardware scheduler can include a schedulingcircuit 308 b/n to implement traffic management of incoming packets.Packets from a certain source, relating to a certain traffic class,pertaining to a specific application or flowing to a certain socket arereferred to as part of a session flow and are classified using sessionmetadata. Such classification can be performed by classifier 308 e.

In some embodiments, session metadata 308 d can serve as the criterionby which packets are prioritized and scheduled and as such, incomingpackets can be reordered based on their session metadata. Thisreordering of packets can occur in one or more buffers and can modifythe traffic shape of these flows. The scheduling discipline chosen forthis prioritization, or traffic management (TM), can affect the trafficshape of flows and micro-flows through delay (buffering), bursting oftraffic (buffering and bursting), smoothing of traffic (buffering andrate-limiting flows), dropping traffic (choosing data to discard so asto avoid exhausting the buffer), delay jitter (temporally shifting cellsof a flow by different amounts) and by not admitting a connection (e.g.,cannot simultaneously guarantee existing service level agreements (SLAs)with an additional flow's SLA).

According to embodiments, computational unit 300 can serve as part of aswitch fabric, and provide traffic management with depth-limited outputqueues, the access to which is arbitrated by a scheduling circuit 308b/n. Such output queues are managed using a scheduling discipline toprovide traffic management for incoming flows. The session flows queuedin each of these queues can be sent out through an output port to adownstream network element.

It is noted that conventional traffic management do not take intoaccount the handling and management of data by downstream elementsexcept for meeting the SLA agreements it already has with saiddownstream elements.

In contrast, according to embodiments a scheduler circuit 308 b/n canallocate a priority to each of the output queues and carry outreordering of incoming packets to maintain persistence of session flowsin these queues. A scheduler circuit 308 b/n can be used to control thescheduling of each of these persistent sessions into a general purposeoperating system (OS) 308 j, executed on an offload processor 308 i.Packets of a particular session flow, as defined above, can belong to aparticular queue. The scheduler circuit 308 b/n may control theprioritization of these queues such that they are arbitrated forhandling by a general purpose (GP) processing resource (e.g., offloadprocessor 308 i) located downstream. An OS 308 j running on a downstreamprocessor 308 i can allocate execution resources such as processorcycles and memory to a particular queue it is currently handling. The OS308 j may further allocate a thread or a group of threads for thatparticular queue, so that it is handled distinctly by the generalpurpose processing element 308 i as a separate entity. The fact thatthere can be multiple sessions running on a GP processing resource, eachhandling data from a particular session flow resident in a queueestablished by the scheduler circuit, tightly integrates the schedulerand the downstream resource (e.g., 308 i). This can bring aboutpersistence of session information across the traffic management andscheduling circuit and the general purpose processing resource 308 i.

Dedicated computing resources (e.g., 308 i), memory space and sessioncontext information for each of the sessions can provide a way ofhandling, processing and/or terminating each of the session flows at thegeneral purpose processor 308 i. The scheduler circuit 308 b/n canexploit this functionality of the execution resource to queue sessionflows for scheduling downstream. The scheduler circuit 308 b/n can beinformed of the state of the execution resource(s) (e.g., 308 i), thecurrent session that is run on the execution resource; the memory spaceallocated to it, the location of the session context in the processorcache.

According to embodiments, a scheduler circuit 308 b/n can furtherinclude switching circuits to change execution resources from one stateto another. The scheduler circuit 308 b/n can use such a capability toarbitrate between the queues that are ready to be switched into thedownstream execution resource. Further, the downstream executionresource can be optimized to reduce the penalty and overhead associatedwith context switch between resources. This is further exploited by thescheduler circuit 308 b/n to carry out seamless switching betweenqueues, and consequently their execution as different sessions by theexecution resource.

According to embodiments, a scheduler circuit 308 b/n can scheduledifferent sessions on a downstream processing resource, wherein the twoare operated in coordination to reduce the overhead during contextswitches. An important factor in decreasing the latency of services andengineering computational availability can be hardware context switchingsynchronized with network queuing. In embodiments, when a queue isselected by a traffic manager, a pipeline coordinates swapping in of thecache (e.g., L2 cache) of the corresponding resource (e.g., 308 i) andtransfers the reassembled I/O data into the memory space of theexecuting process. In certain cases, no packets are pending in thequeue, but computation is still pending to service previous packets.Once this process makes a memory reference outside of the data swapped,the scheduler circuit (308 b/n) can enable queued data from an I/Odevice 302 to continue scheduling the thread.

In some embodiments, to provide fair queuing to a process not havingdata, a maximum context size can be assumed as data processed. In thisway, a queue can be provisioned as the greater of computational resourceand network bandwidth resource. As but one very particular example, acomputation resource can be an ARM A9 processor running at 800 MHz,while a network bandwidth can be 3 Gbps of bandwidth. Given the lopsidednature of this ratio, embodiments can utilize computation having manyparallel sessions (such that the hardware's prefetching ofsession-specific data offloads a large portion of the host processorload) and having minimal general purpose processing of data.

Accordingly, in some embodiments, a scheduler circuit 308 b/n can beconceptualized as arbitrating, not between outgoing queues at line ratespeeds, but arbitrating between terminated sessions at very high speeds.The stickiness of sessions across a pipeline of stages, including ageneral purpose OS, can be a scheduler circuit optimizing any or allsuch stages of such a pipeline.

Alternatively, a scheduling scheme can be used as shown in U.S. Pat. No.7,760,715 issued to Dalal on Jul. 20, 2010, incorporated herein byreference. This scheme can be useful when it is desirable to rate limitthe flows for preventing the downstream congestion of another resourcespecific to the over-selected flow, or for enforcing service contractsfor particular flows. Embodiments can include arbitration scheme thatallows for service contracts of downstream resources, such as generalpurpose OS that can be enforced seamlessly.

Referring still to FIG. 3, a hardware scheduler according to embodimentsherein, or equivalents, can provide for the classification of incomingpacket data into session flows based on session metadata. It can furtherprovide for traffic management of these flows before they are arbitratedand queued as distinct processing entities on the offload processors.

In some embodiments, offload processors (e.g., 308 i) can be generalpurpose processing units capable of handling packets of differentapplication or transport sessions. Such offload processors can be lowpower processors capable of executing general purpose instructions. Theoffload processors could be any suitable processor, including but notlimited to: ARM, ARC, Tensilica, MIPS, StrongARM or any other processorthat serves the functions described herein. Such offload processors havea general purpose OS running on them, wherein the general purpose OS isoptimized to reduce the penalty associated with context switchingbetween different threads or group of threads.

In contrast, context switches on host processors can be computationallyintensive processes that require the register save area, process contextin the cache and TLB entries to be restored if they are invalidated oroverwritten. Instruction Cache misses in host processing systems canlead to pipeline stalls and data cache misses lead to operation stalland such cache misses reduce processor efficiency and increase processoroverhead.

Also in contrast, an OS 308 j running on the offload processors 308 i inassociation with a scheduler circuit 308 b/n, can operate together toreduce the context switch overhead incurred between different processingentities running on it. Embodiments can include a cooperative mechanismbetween a scheduler circuit and the OS on the offload processor 308 i,wherein the OS sets up session context to be physically contiguous(physically colored allocator for session heap and stack) in the cache;then communicates the session color, size, and starting physical addressto the scheduler circuit upon session initialization. During an actualcontext switch, a scheduler circuit can identify the session context inthe cache by using these parameters and initiate a bulk transfer ofthese contents to an external low latency memory (e.g., 308 g). Inaddition, the scheduler circuit can manage the prefetch of the oldsession if its context was saved to a local memory 308 g. In particularembodiments, a local memory 308 g can be low latency memory, such as areduced latency dynamic random access memory (RLDRAM), as but one veryparticular embodiment. Thus, in embodiments, session context can beidentified distinctly in the cache.

In some embodiments, context size can be limited to ensure fastswitching speeds. In addition or alternatively, embodiments can includea bulk transfer mechanism to transfer out session context to a localmemory 308 g. The cache contents stored therein can then be retrievedand prefetched during context switch back to a previous session.Different context session data can be tagged and/or identified withinthe local memory 308 g for fast retrieval. As noted above, contextstored by one offload processor may be recalled by a different offloadprocessor.

In the very particular embodiment of FIG. 3, multiple offload processingcores can be integrated into a computation FPGA 308. Multiplecomputational FPGAs can be arbitrated by arbitrator circuits in anotherFPGA 310. The combination of computational FPGAs (e.g., 308) and arbiterFPGAs (e.g., 310) are referred to as “XIMM” modules or “Xockets DIMMmodules” (e.g., computation unit 300). In particular applications, theseXIMM modules can provide integrated traffic and thread managementcircuits that broker execution of multiple sessions on the offloadprocessors.

FIG. 3 also shows an offload processor tunnel connection 308 k, as wellas a memory interface 308 m and port 3081 (which can be an acceleratorcoherency port (ACP)). Memory interface 308 m can access buffer memory308 a.

Having described various embodiments suitable for full bandwidth packethandling management operations, two example illustrating particularconfigurations for embodiments will now be described.

EXAMPLE 1

FIG. 4 illustrates a system 400 according to an embodiment. A system 400can perform “Map-Reduce” based data search and analysis operations. Asystem 400 can provide data-intensive parallel computation in multipleservers or clusters of servers. In very particular embodiments, serverscan be mounted in a same rack or different racks.

In one embodiment, a Map-Reduce data processing can be Hadoop, an opensource program implemented in Java. Example applications for Hadoop orother Map-Reduce based data processing systems include processingcrawled documents, web request log analysis, or general unstructureddata search and analysis. In Map-Reduce data processing, data isinitially partitioned across the nodes of a cluster and stored in adistributed file system (DFS). Data is represented as (key, value)pairs. The computation is expressed using two functions:Map(k1,v1)→list(k2,v2);Reduce(k2,list(v2))→list(v3)

Input data can be partitioned and Map functions can be applied inparallel on all the partitions (called “splits”). A mapper is initiatedfor each of the partitions, which applies the map function to all theinput (key, value) pairs. The results from all the mappers are mergedinto a single sorted stream. At each receiving node, a reducer fetchesall of its sorted partitions during the shuffle phase and merges theminto a single sorted stream. All the pair values that share a certainkey are passed to a single reduce call. The output of each Reducefunction is written to a distributed file in a distributed file system(DFS). A master node runs a JobTracker which organizes the cluster'sactivities. Each of the worker nodes runs a TaskTracker which organizesthe worker node's activities. All input jobs are organized intosequential tiers of map tasks and reduce tasks. The TaskTracker runs anumber of map and reduce tasks concurrently and pulls new tasks from theJobTracker as soon as the old tasks are completed. All the nodescommunicate results from the Map-Reduce operations in the form of blocksof data over the network using an HTTP based protocol. Inimplementations based on Hadoop, the Map-Reduce layer storesintermediate data produced by the map and reduce tasks in the HadoopDistributed File System (HDFS). HDFS is designed to provide highstreaming throughput to large, write-once-read-many-times files.

Hadoop can be more efficiently operated using inter-server communicationand does not require redirection with a Top-of-Rack (TOR) switch.Servers equipped with offload processor modules, such as describedherein and equivalent, can bypass a TOR switch through intelligentvirtual switching of the offload processor modules associated with eachserver. In particular embodiments, such bypassing of a TOR switch canallow for improvements in processing of a shuffling step (communicationof map results to the reducers).

Referring to FIG. 4, an input section 474 can fetch input data from afile system 488. Input data can be partitioned into splits 476) andparsed into records that contain initial (key, value) pairs 478. FIG. 4shows how mappers 450 can operate map functions on splits 476. Theresults are communicated to the reducers 480 (shuffling 482/484).Instead of using HTTP for such communication, as can occur inconventional systems, shuffling can be performed using a“publish-subscribe” scheme. The results from the map steps are obtainedby offload processor modules by performing DMA operations on the mainmemory of a system. The (key, value) pairs are then parsed by theoffload processor modules and the key is published through a midplaneswitch fabric. Keys are identified (e.g., through content addressablememory), and forwarded to reducers 480 via virtual interrupts. Themidplane switch can be collectively defined by offload processormodules. In particular embodiments, such a switch can drive and receivean entire system bus bandwidth (e.g., PCI-e 3.0 bandwidth of 240 Gbps),tightly connecting map steps with reduce steps. It is understood thatsuch connected map steps can be via intra-rack communication and/orinter-rack communication.

EXAMPLE 2

Inter- and intra-rack communication according to embodiments describedherein, or equivalents, can be useful for complex transport, databandwidth intensive, frequent random access oriented, “light touch”processing loads for execution by multiple offload processor cores. Insome embodiments, such processing tasks may not even require processingresources of a server processor core (e.g., ×86 processor cores). Incertain embodiments, such tasks may not require access to a serverprocessor through a conventional I/O fabric, since an offload processormodule can include suitable IO ports for Ethernet-like, Infiniband, orother commonly available rack or cluster interconnects. One suchembodiment is shown in FIG. 5.

FIG. 5 shows a system 500 according to embodiment. A system 500 caninclude a server 502 that supports a memory bus connected offloadprocessor module 504. The offload processor module 504 is able todirectly process network packet operations, including but not limited tovarious network overlay operations on packet or other I/O data, withoutnecessarily requiring any control by host processor(s) or memory of theserver 502.

Packet or other I/O data can be received at an I/O device 520. An I/Odevice 520 can be physical device, virtual device or combinationthereof. An IOMMU 521 can map received data to physical addresses of asystem address space. DMA master 525 can transmit such data to suchmemory addresses by operation of a memory controller 522. Memorycontroller 522 can execute data transfers over a memory bus 528 with aDMA Slave 527. Upon receiving transferred I/O data, a hardware scheduler523 can schedule processing of such data with an offload processor 526.In some embodiments, a type of processing can be indicated by metadatawithin the I/O data. Further, in some embodiments such data can bestored in an onboard memory 529. According to instructions from hardwarescheduler 523, one or more offload processors 526 can execute computingfunctions in response to the I/O data, including but not limited tooperations on packets redirected for network overlay. In someembodiments, such computing functions can operate on the I/O data, andsuch data can be subsequently read out on memory bus via a read requestprocessed by DMA Slave 527.

FIG. 6 is a flow chart illustrating an exemplary method 600 of providinginter- and intra-rack communication network overlay services, withoutaccess to server resources including I/O, memory, or processors. Datacan be received from a physical network (602). Such an action caninclude receiving data from a network, including but limited to a wired,wireless, optical, switched, or packet based network. Packets can bechecked for a logical network identifier (604). Packets without alogical network identifier (or without particular logical networkidentifier(s)) (No from 604) can be transported as required by protocol(step 606).

Packets with a logical network identifier (Yes from 604) can besegregated for further processing. Such processing can includedetermination of a particular virtual target (607) on the network, andthe appropriate translation into a physical memory space (608). Thepacket is then sent to a physical memory address space used by anoffload processor (610). Packets can be transformed (612) in some mannerby the offload processor, and transported back onto the physical networkwithout using server resources (614).

It should be appreciated that in the foregoing description of exemplaryembodiments of the invention, various features of the invention aresometimes grouped together in a single embodiment, figure, ordescription thereof for the purpose of streamlining the disclosureaiding in the understanding of one or more of the various inventiveaspects. This method of disclosure, however, is not to be interpreted asreflecting an intention that the claimed invention requires morefeatures than are expressly recited in each claim. Rather, as thefollowing claims reflect, inventive aspects lie in less than allfeatures of a single foregoing disclosed embodiment. Thus, the claimsfollowing the detailed description are hereby expressly incorporatedinto this detailed description, with each claim standing on its own as aseparate embodiment of this invention.

It is also understood that the embodiments of the invention may bepracticed in the absence of an element and/or step not specificallydisclosed. That is, an inventive feature of the invention may beelimination of an element.

Accordingly, while the various aspects of the particular embodiments setforth herein have been described in detail, the present invention couldbe subject to various changes, substitutions, and alterations withoutdeparting from the spirit and scope of the invention.

What is claimed is:
 1. A distributed system server system for a packetprocessing without top of rack (TOR) units, comprising: a plurality ofservers, each server having at least one host processor, and a pluralityof offload processor modules, each offload processor module beingconnected to memory bus on the server and having an input-output (IO)port and multiple offload processors, each offload processor module alsoconfigured to receive network packets from one of the servers through amemory bus and the IO port on the offload processing module; and amemory controller configured to send network packet data directly to atleast one offload processor module via the memory bus to which theoffload processor module is attached; wherein a first of the offloadprocessor modules is connected directly to a second of the offloadprocessor modules through the IO ports of the first and second processormodules, the first and second offload processor modules configured toprovide bidirectional network packet flow between at least the first andsecond offload processor modules and at least one host processor withoutuse of a TOR unit for an inter-server connection.
 2. The system of claim1 wherein each offload processor module is physically connected to amemory socket on a server.
 3. The system of claim 1 wherein each offloadprocessor module is physically connected to a dual in line memory module(DIMM) socket on a server.
 4. The system of claim 1 wherein multipleoffload processor modules are connected to multiple memory sockets on atleast one of the servers.
 5. The system of claim 1 wherein at least oneoffload processor module is configured as a midplane switch to forwardnetwork packets to one or more of the multiple offload processormodules.
 6. The system of claim 1 wherein the offload processors areconfigured to direct network packets to one or more of the offloadprocessors based on availability of the offload processors.
 7. Thesystem of claim 1, wherein each offload processor module furthercomprises a scheduler configured to determine a routing of networkpackets through respective IO ports of the offload processor modulesbased on availability of offload processors.